Dual Consistency-enhanced Semi-supervised Sentiment Analysis towards COVID-19 Tweets

In the context of COVID-19, numerous people present their opinions through social networks. It is thus highly desired to conduct sentiment analysis towards COVID-19 tweets to learn the public's attitudes, and facilitate the government to make proper guidelines for avoiding the social unrest. Although many efforts have studied the text-based sentiment classification from various domains (e.g., delivery and shopping reviews), it is hard to directly use these classifiers for the sentiment analysis towards COVID-19 tweets due to the domain gap. In fact, developing the sentiment classifier for COVID-19 tweets is mainly challenged by the limited annotated training dataset, as well as the diverse and informal expressions of user-generated posts. To address these challenges, we construct a large-scale COVID-19 dataset from Weibo and propose a dual COnsistency-enhanced semi-superVIseD network for Sentiment Anlaysis (COVID-SA). In particular, we first introduce a knowledge-based augmentation method to augment data and enhance the model's robustness. We then employ BERT as the text encoder backbone for both labeled data, unlabeled data, and augmented data. Moreover, we propose a dual consistency (i.e., label-oriented consistency and instance-oriented consistency) regularization to promote the model performance. Extensive experiments on our self-constructed dataset and three public datasets show the superiority of COVID-SA over state-of-the-art baselines on various applications. IEEE

Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster

Human mobility nowcasting is a fundamental research problem for intelligent transportation planning, disaster responses and management, etc. In particular, human mobility under big disasters such as hurricanes and pandemics deviates from its daily routine to a large extent, which makes the task more challenging. Existing works mainly focus on traffic or crowd flow prediction in normal situations. To tackle this problem, in this study, disaster-related Twitter data is incorporated as a covariate to understand the public awareness and attention about the disaster events and thus perceive their impacts on the human mobility. Accordingly, we propose a Meta-knowledge-Memorizable Spatio-Temporal Network (MemeSTN), which leverages memory network and meta-learning to fuse social media and human mobility data. Extensive experiments over three real-world disasters including Japan 2019 typhoon season, Japan 2020 COVID-19 pandemic, and US 2019 hurricane season were conducted to illustrate the effectiveness of our proposed solution. Compared to the state-of-the-art spatio-temporal deep models and multivariate-time-series deep models, our model can achieve superior performance for nowcasting human mobility in disaster situations at both country level and state level. © 2023 ACM.

Epidemiological characteristics of four times of community transmission of COVID-19 in Hebei

Objective To analyze the epidemiological characteristics of community transmission of the coronavirus disease 2019 (COVID-19) caused by four imported cases in Hebei Province, and to provide a scientific basis for the prevention and control of the disease. Methods Descriptive epidemiological methods were used to analyze the epidemiological characteristics of four community-transmitted COVID-19 outbreaks reported in the China Disease Control and Prevention Information System from January 1, 2020 to December 31, 2021 in Hebei Province. Results From January 1, 2020 to December 31, 2021, four community-transmitted COVID-19 outbreaks caused by imported COVID-19 occurred in Hebei Province, respectively related of Hubei (Wuhan) Province, Beijing Xinfadi market, Overseas cases and Ejina banner of Inner Mongolia Autonomous Region. Total of 1 656 cases (1 420 confirmed cases and 236 asymptomatic cases) were reported, including 375 cases in phase A (From January 22 to April 16, 2020), and phase B (from June 14 to June 24, 2020) 27 cases were reported, with 1 116 cases reported in the third phase (Phase C, January 2 to February 14, 2021), and 138 cases reported in the fourth phase (Phase D, October 23 to November 14, 2021). The 1 656 cases were distributed in 104 counties of 11 districts (100.00%), accounting for 60.46% of the total number of counties in the province. There were 743 male cases and 913 female cases, with a male to female ratio of 0.81:1. The minimum age was 13 days, the maximum age was 94 years old, and the average age (median) was 40.3 years old. The incidence was 64.01% between 30 and 70 years old. Farmers and students accounted for 54.41% and 14.73% of the total cases respectively. Of the 1 420 confirmed cases, 312 were mild cases, accounting for 21.97%;Common type 1 095 cases (77.11%);There was 1 severe case and 12 critical cases, accounting for 0.07% and 0.85%, respectively. 7 patients died from 61.0 to 85.7 years old. The mean (median) time from onset to diagnosis was 1.9 days (0-31 days), and the mean (median) time of hospital stay was 15 days (1.5-56 days). Conclusions Four times in Hebei province COVID-19 outbreak in scale, duration, population, epidemic and type of input source, there are some certain difference, but there are some common characteristics, such as the outbreak occurs mainly during the legal holidays or after starting and spreading epidemic area is mainly in rural areas, aggregation epidemic is the main mode of transmission, etc. To this end, special efforts should be made to strengthen the management of people moving around during holidays, and strengthen the implementation of epidemic prevention and control measures in places with high concentration of people. To prevent the spread of the epidemic, we will step up surveillance in rural areas, farmers' markets, medical workers and other key areas and groups, and ensure early detection and timely response.Copyright © 2022 China Tropical Medicine. All rights reserved.

Prediction Model for Inclusive Finance Development Considering the Impact of COVID-19: The Case of China

Models in previous studies about inclusive finance often include economic data while excludes public online statements. In this paper Random Forest Regression (RFR) model is trained on the annual influencing factors and annual financial inclusion index to predict quarterly financial inclusion index by the quarterly influencing factors to expand the size of data. Then, BOW model tf-idf algorithm is used to convert COVID-19 – loan related online statements into word vectors. Lastly, these influencing factors of different lag periods are passed into the RFR model to compare their performance. Result of models shows that there is impact the epidemic has on the development of inclusive finance, and the lag period of the impact opinion texts on financial inclusion is 2 quarters. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The Impact of COVID-19 on Human Mobility: A Case Study on New York

COVID-19 has dramatically changed people's mobility patterns. This report aims to analyze the impact of COVID-19 on people's mobility through statistics and comparing the visits of POIs (Point-Of-Interests) in New York State in 2019 and 2020. The report uses data from SafeGraph, which is a data company. The raw data contains POI visits across the United States in 2019 and 2020. Considering the analysis size and difficulty of the data, POI visits from New York State are extracted for analysis, and POI locations are classified according to the tags provided by the source data. The scale of analysis is from macro to micro, and they are the total POI visits data of New York State based on different ways in 2019 and 2020, the POI visits of CBG (Census Block Group) division in New York City, and three representative POI samples to do individual analysis. The analysis methods are: (1) use line plot and bar plot statistics to compare the trends of POI visits data from 2019 to 2020, and (2) make the spatial visualization comparison, which includes grid map, scatter map, heatmap, and OD map, between the first peak of epidemic impact in the first full week of April 2019 and April 2020, and the scope is narrowed to New York City. Wherein the OD maps are drawn based on the CBG division. Compared to related work, the analysis object includes CBG, categories, and individual POI. In addition, the analysis method combines statistical graphs and spatial visualizations and explores the policy impact of the New York City government. This report adopts more multidimensional analysis methods and objects to improve the comprehensiveness and reliability of the analysis content. © 2022 IEEE.

Human Mobility-based Individual-level Epidemic Simulation Platform

COVID-19 has spread worldwide, and over 140 million people have been confirmed infected, over 3 million people have died, and the numbers are still increasing dramatically. The consensus has been reached by scientists that COVID-19 can be transmitted in an airborne way, and human-to-human transmission is the primary cause of the fast spread of COVID-19. Thus, mobility should be restricted to control the epidemic, and many governments worldwide have succeeded in curbing the spread by means of control policies like city lockdowns. Against this background, we propose a novel fine-grained transmission model based on real-world human mobility data and develop a platform that helps the researcher or governors to explore the possibility of future development of the epidemic spreading and simulate the outcomes of human mobility and the epidemic state under different epidemic control policies. The proposed platform can also support users to determine potential contacts, discover regions with high infectious risks, and assess the individual infectious risk. The multi-functional platform aims at helping the users to evaluate the effectiveness of a regional lockdown policy and facilitate the process of screening and more accurately targeting the potential virus carriers. © 2022 held by the owner/author(s). Publication rights licensed to ACM.

MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks

Epidemic prediction is a fundamental task for epidemic control and prevention. Many mechanistic models and deep learning models are built for this task. However, most mechanistic models have difficulty estimating the time/region-varying epidemiological parameters, while most deep learning models lack the guidance of epidemiological domain knowledge and interpretability of prediction results. In this study, we propose a novel hybrid model called MepoGNN for multi-step multi-region epidemic forecasting by incorporating Graph Neural Networks (GNNs) and graph learning mechanisms into Metapopulation SIR model. Our model can not only predict the number of confirmed cases but also explicitly learn the epidemiological parameters and the underlying epidemic propagation graph from heterogeneous data in an end-to-end manner. Experiment results demonstrate our model outperforms the existing mechanistic models and deep learning models by a large margin. Furthermore, the analysis on the learned parameters demonstrates the high reliability and interpretability of our model and helps better understanding of epidemic spread. Our model and data have already been public on GitHub https://github.com/deepkashiwa20/MepoGNN.git. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A review of the immune activity of chitooligosaccharides

Under the influence of the COVID-19, people's awareness of physical health and immunity has increased significantly. Chitooligosaccharide is an oligomer of β-(1, 4)-linked D-glucosamine, furthermore, is one of the most widely studied immunomodulators. Chitooligosaccharide can be prepared from the chitin or chitosan polymers through enzymatically, chemically or physically processes. Chitooligosaccharide and its derivatives have been proven to have a wide range of biological activities including intestinal flora regulation, immunostimulant, anti-tumor, anti-obesity and anti-oxidation effects. This review summarizes the latest research of the preparation methods, biological activities in immunity and safety profiles of Chitooligosaccharide and its derivatives. We recapped the effect mechanisms of Chitooligosaccharide basing on overall immunity. Comparing the effects of Chitooligosaccharide with different molecular weights and degree of aggregation, a reference range for usage has been provided. This may provide a support for the application of Chitooligosaccharide in immune supplements and food. In addition, future research directions are also discussed. © 2023, Sociedade Brasileira de Ciencia e Tecnologia de Alimentos, SBCTA. All rights reserved.

Global spread of carbapenem-resistant Enterobacteriaceae: Epidemiological features, resistance mechanisms, detection and therapy

Bacterial drug resistance has become a global public health threat, among which the infection of carbapenem-resistant Enterobacterales (CRE) is one of the top noticeable issues in the global anti-infection area due to limited therapy options. In recent years, the prevalence of CRE transmission around the world has increased, and the transmission of COVID-19 has intensified the situation to a certain extent. CRE resistance can be induced by carbapenemase, porin, efflux pump, penicillin-binding protein alteration, and biofilm production. Deletion, mutation, insertion, and post-transcriptional modification of corresponding coding genes may affect the sensitivity of Enterobacterales bacteria to carbapenems. Clinical and laboratory methods to detect CRE and explore its resistance mechanisms are being developed. Due to the limited options of antibiotics, the clinical treatment of CRE infection also faces severe challenges. The clinical therapies of CRE include single or combined use of antibiotics, and some new antibiotics and treatment methods are also being developed. Hence, this review summarizes the epidemiology, resistance mechanisms, screening and clinical treatments of CRE infection, to provide references for clinical prevention, control and treatment of CRE infection. © 2022 Elsevier GmbH

Effect of COVID-19 lockdown on the characterization and mixing state of carbonaceous particles in the urban atmosphere of Liaocheng, the North China Plain

To investigate the effect of COVID-19 control measures on aerosol chemistry, the chemical compositions, mixing states, and formation mechanisms of carbonaceous particles in the urban atmosphere of Liaocheng in the North China Plain (NCP) were compared before and during the pandemic using a single particle aerosol mass spectrometry (SPAMS). The results showed that the concentrations of five air pollutants including PM2.5, PM10, SO2, NO2, and CO decreased by 41.2%-71.5% during the pandemic compared to those before the pandemic, whereas O3 increased by 1.3 times during the pandemic because of the depressed titration of O3 and more favorable meteorological conditions. The count and percentage contribution of carbonaceous particles in the total detected particles were lower during the pandemic than those before the pandemic. The carbonaceous particles were dominated by elemental and organic carbon (ECOC, 35.9%), followed by elemental carbon-aged (EC-aged, 19.6%) and organic carbon-fresh (OCfresh, 13.5%) before the pandemic, while EC-aged (25.3%), ECOC (17.9%), and secondary ions-rich (SEC, 17.8%) became the predominant species during the pandemic. The carbonaceous particle sizes during the pandemic showed a broader distribution than that before the pandemic, due to the condensation and coagulation of carbonaceous particles in the aging processes. The relative aerosol acidity (Rra) was smaller before the pandemic than that during the pandemic, indicating the more acidic particle aerosol during the pandemic closely related to the secondary species and relative humidity (RH). More than 95.0% and 86.0% of carbonaceous particles in the whole period were internally mixed with nitrate and sulfate, implying that most of the carbonaceous particles were associated with secondary oxidation during their formation processes. The diurnal variations of oxalate particles and correlation analyses suggested that oxalate particles before the pandemic were derived from aqueous oxidation driven by RH and liquid water content (LWC), while oxalate particles during the pandemic were originated from O3dominated photochemical oxidation.(c) 2022 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

Indoor environmental quality and pollutant dispersion estimation inside a bus at the downtown areas of Dalian, China

Among most public transport modes, the frequent start-stop urban bus has the most complex micro-environment. Indoor environment quality, airflow patterns, etc. has not been fully understood yet inside buses. In addition, under COVID-19 pandemic, it had been proved aerosol transmission risk might be enhanced inside the buses. Usually, carbon dioxide (CO2) could be considered the index of ventilation effect in enclosed environment, airborne particles are viral carriers. Thus, accurate forecasting of the two abovementioned key pollutants become important. The study analysed the CO2 and airborne particle dispersion inside a bus at the downtown areas of Dalian, China by employing field measurement at spring and autumn, 2021. Temperature, relative humidity, CO2 and airborne particle concentrations were logged by sensors at sampling points respectively, passengers onboard were counted manually. Correlation analysis was conducted and two empirical models for evaluating CO2 and airborne particle were concluded based on the measurement data. From preliminary results, transient concentration of pollutant is almost linearly correlated with cumulative and instant numbers of passenger respectively, with Pearson correlation coefficient larger than 0.8336 for CO2 and 0.8424 for PM2.5. The purpose of the study is to reflect environmental quality inside the bus and provide inspiration into pollution control strategies in buses. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/)

AI-Enabled Health 4.0: An IoT-Based COVID-19 Diagnosis Use-Case

The Internet of Things (IoT) has revamped service-oriented architectures by enabling edge-based devices to collect and share information that is vital for the service provisioning process. IoT devices have evolved from simple data acquirers and have become part of the service provisioning process. These devices are now able to sense, acquire, communicate, and process data in an intelligent manner. With the support of Artificial Intelligence (AI), IoT devices can now support users with minimal reliance on centralized entities, such as the Cloud. IoT devices are now able to share raw and processed information securely, without or with minimal reliance on centralized devices. This paper proposes a general framework for Health 4.0 to provide edge-based health services with the support of AI. IoT devices collect and share patient information in a secure manner to enable user-side disease diagnosis. The solution enables both federated and centralized learning to coexist under one framework. As a proof-of-concept, the solution considers a COVID-19 diagnosis use-case. A Machine Learning (ML) web-based user application is developed to analyze frontal chest X-ray (CXR) images and make predictions on whether patients' lungs are damaged. The solution provides an experimental study on mechanisms and approaches needed to increase learning accuracy with reduced dataset sizes and image quality through Federated Learning (FL). © 2022 IEEE.

A review of the immune activity of chitooligosaccharides

Under the influence of the COVID-19, people's awareness of physical health and immunity has increased significantly. Chitooligosaccharide is an oligomer of β-(1, 4)-linked D-glucosamine, furthermore, is one of the most widely studied immunomodulators. Chitooligosaccharide can be prepared from the chitin or chitosan polymers through enzymatically, chemically or physically processes. Chitooligosaccharide and its derivatives have been proven to have a wide range of biological activities including intestinal flora regulation, immunostimulant, anti-tumor, anti-obesity and anti-oxidation effects. This review summarizes the latest research of the preparation methods, biological activities in immunity and safety profiles of Chitooligosaccharide and its derivatives. We recapped the effect mechanisms of Chitooligosaccharide basing on overall immunity. Comparing the effects of Chitooligosaccharide with different molecular weights and degree of aggregation, a reference range for usage has been provided. This may provide a support for the application of Chitooligosaccharide in immune supplements and food. In addition, future research directions are also discussed. © 2023, Sociedade Brasileira de Ciencia e Tecnologia de Alimentos, SBCTA. All rights reserved.

A Survey on Data-driven COVID-19 and Future Pandemic Management

The COVID-19 pandemic has resulted in more than 440 million confirmed cases globally and almost 6 million reported deaths as of March 2022. Consequently, the world experienced grave repercussions to citizens' lives, health, wellness, and the economy. In responding to such a disastrous global event, countermeasures are often implemented to slow down and limit the virus's rapid spread. Meanwhile, disaster recovery, mitigation, and preparation measures have been taken to manage the impacts and losses of the ongoing and future pandemics. Data-driven techniques have been successfully applied to many domains and critical applications in recent years. Due to the highly interdisciplinary nature of pandemic management, researchers have proposed and developed data-driven techniques across various domains. However, a systematic and comprehensive survey of data-driven techniques for pandemic management is still missing. In this article, we review existing data analysis and visualization techniques and their applications for COVID-19 and future pandemic management with respect to four phases (namely, Response, Recovery, Mitigation, and Preparation) in disaster management. Data sources utilized in these studies and specific data acquisition and integration techniques for COVID-19 are also summarized. Furthermore, open issues and future directions for data-driven pandemic management are discussed.

Epidemiological characteristics of four times of community transmission of COVID-19 in Hebei

Objective To analyze the epidemiological characteristics of community transmission of the coronavirus disease 2019 (COVID-19) caused by four imported cases in Hebei Province, and to provide a scientific basis for the prevention and control of the disease. Methods Descriptive epidemiological methods were used to analyze the epidemiological characteristics of four community-transmitted COVID-19 outbreaks reported in the China Disease Control and Prevention Information System from January 1, 2020 to December 31, 2021 in Hebei Province. Results From January 1, 2020 to December 31, 2021, four community-transmitted COVID-19 outbreaks caused by imported COVID-19 occurred in Hebei Province, respectively related of Hubei (Wuhan) Province, Beijing Xinfadi market, Overseas cases and Ejina banner of Inner Mongolia Autonomous Region. Total of 1 656 cases (1 420 confirmed cases and 236 asymptomatic cases) were reported, including 375 cases in phase A (From January 22 to April 16, 2020), and phase B (from June 14 to June 24, 2020) 27 cases were reported, with 1 116 cases reported in the third phase (Phase C, January 2 to February 14, 2021), and 138 cases reported in the fourth phase (Phase D, October 23 to November 14, 2021). The 1 656 cases were distributed in 104 counties of 11 districts (100.00%), accounting for 60.46% of the total number of counties in the province. There were 743 male cases and 913 female cases, with a male to female ratio of 0.81∶1. The minimum age was 13 days, the maximum age was 94 years old, and the average age (median) was 40.3 years old. The incidence was 64.01% between 30 and 70 years old. Farmers and students accounted for 54.41% and 14.73% of the total cases respectively. Of the 1 420 confirmed cases, 312 were mild cases, accounting for 21.97%;Common type 1 095 cases (77.11%);There was 1 severe case and 12 critical cases, accounting for 0.07% and 0.85%, respectively. 7 patients died from 61.0 to 85.7 years old. The mean (median) time from onset to diagnosis was 1.9 days (0-31 days), and the mean (median) time of hospital stay was 15 days (1.5-56 days). Conclusions Four times in Hebei province COVID-19 outbreak in scale, duration, population, epidemic and type of input source, there are some certain difference, but there are some common characteristics, such as the outbreak occurs mainly during the legal holidays or after starting and spreading epidemic area is mainly in rural areas, aggregation epidemic is the main mode of transmission, etc. To this end, special efforts should be made to strengthen the management of people moving around during holidays, and strengthen the implementation of epidemic prevention and control measures in places with high concentration of people. To prevent the spread of the epidemic, we will step up surveillance in rural areas, farmers′ markets, medical workers and other key areas and groups, and ensure early detection and timely response. © 2022 China Tropical Medicine. All rights reserved.

Human Mobility-based Individual-level Epidemic Simulation Platform

COVID-19 has spread worldwide, and over 140 million people have been confirmed infected, over 3 million people have died, and the numbers are still increasing dramatically. The consensus has been reached by scientists that COVID-19 can be transmitted in an airborne way, and human-to-human transmission is the primary cause of the fast spread of COVID-19. Thus, mobility should be restricted to control the epidemic, and many governments worldwide have succeeded in curbing the spread by means of control policies like city lockdowns. Against this background, we propose a novel fine-grained transmission model based on realworld human mobility data and develop a platform that helps the researcher or governors to explore the possibility of future development of the epidemic spreading and simulate the outcomes of human mobility and the epidemic state under different epidemic control policies. The proposed platform can also support users to determine potential contacts, discover regions with high infectious risks, and assess the individual infectious risk. The multi-functional platform aims at helping the users to evaluate the effectiveness of a regional lockdown policy and facilitate the process of screening and more accurately targeting the potential virus carriers.

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation

To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract fine-grained multimedia knowledge elements (entities and their visual chemical structures, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence. All of the data, KGs, reports(1), resources, and shared services are publicly available(2).

Experience of medical rescue support and nursing in space station mission

Objective To explore the characteristics and nursing measures of medical rescue support in space station. Methods According to the characteristics of medical rescue support task of manned space mission, practical nursing schemes and corresponding nursing measures were formulated from the aspects of personnel selection, personnel training, material preparation, process formulation, training exercise and epidemic prevention. Results The "Shenzhou-12" mission includes the whole launch phase, operation phase and return phase. There are many tasks that cover a long time. The contents of medical rescue support include personnel's physical and mental quality, technical ability, environmental equipment, the possible injuries of astronauts, and the normalization of COVID-19's task guarantee. In view of this content, a practical nursing scheme was formulated to enable nurses to quickly master and skillfully cooperate with the treatment, win more time for the treatment and improve the overall rescue level. Conclusions Various process plans have been integrated and optimized for medical rescue support of space mission. The equipment carried is advanced, and can be used in various complex environments and complex working conditions. The rescue support personnel have also done their best, kept improving and solidified the state. We should constantly sum up experience, explore and study new problems, and make new achievements in medical rescue support for space missions. © 2022 Chinese Medical Association. All rights reserved.

Anvil - System Architecture and Experiences from Deployment and Early User Operations

Anvil is a new XSEDE advanced capacity computational resource funded by NSF. Designed with a systematic strategy to meet the ever increasing and diversifying research needs for advanced computational capacity, Anvil integrates a large capacity high-performance computing (HPC) system with a comprehensive ecosystem of software, access interfaces, programming environments, and composable services in a seamless environment to support a broad range of current and future science and engineering applications of the nation's research community. Anchored by a 1000-node CPU cluster featuring the latest AMD EPYC 3rd generation (Milan) processors, along with a set of 1TB large memory and NVIDIA A100 GPU nodes, Anvil integrates a multi-tier storage system, a Kubernetes composable subsystem, and a pathway to Azure commercial cloud to support a variety of workflows and storage needs. Anvil was successfully deployed and integrated with XSEDE during the world-wide COVID-19 pandemic. Entering production operation in February 2022, Anvil will serve the nation's science and engineering research community for five years. This paper describes the Anvil system and services, including its various components and subsystems, user facing features, and shares the Anvil team's experience through its early user access program from November 2021 through January 2022. © 2022 Owner/Author.